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A novel design process for selection of attributes for inclusion in discrete choice experiments: case study exploring variation in clinical decision-making about thrombolysis in the treatment of…

Overview of attention for article published in BMC Health Services Research, June 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

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9 tweeters

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18 Mendeley
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Title
A novel design process for selection of attributes for inclusion in discrete choice experiments: case study exploring variation in clinical decision-making about thrombolysis in the treatment of acute ischaemic stroke
Published in
BMC Health Services Research, June 2018
DOI 10.1186/s12913-018-3305-5
Pubmed ID
Authors

Aoife De Brún, Darren Flynn, Laura Ternent, Christopher I. Price, Helen Rodgers, Gary A. Ford, Matthew Rudd, Emily Lancsar, Stephen Simpson, John Teah, Richard G. Thomson

Abstract

A discrete choice experiment (DCE) is a method used to elicit participants' preferences and the relative importance of different attributes and levels within a decision-making process. DCEs have become popular in healthcare; however, approaches to identify the attributes/levels influencing a decision of interest and to selection methods for their inclusion in a DCE are under-reported. Our objectives were: to explore the development process used to select/present attributes/levels from the identified range that may be influential; to describe a systematic and rigorous development process for design of a DCE in the context of thrombolytic therapy for acute stroke; and, to discuss the advantages of our five-stage approach to enhance current guidance for developing DCEs. A five-stage DCE development process was undertaken. Methods employed included literature review, qualitative analysis of interview and ethnographic data, expert panel discussions, a quantitative structured prioritisation (ranking) exercise and pilot testing of the DCE using a 'think aloud' approach. The five-stage process reported helped to reduce the list of 22 initial patient-related factors to a final set of nine variable factors and six fixed factors for inclusion in a testable DCE using a vignette model of presentation. In order for the data and conclusions generated by DCEs to be deemed valid, it is crucial that the methods of design and development are documented and reported. This paper has detailed a rigorous and systematic approach to DCE development which may be useful to researchers seeking to establish methods for reducing and prioritising attributes for inclusion in future DCEs.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 22%
Researcher 4 22%
Student > Doctoral Student 3 17%
Unspecified 2 11%
Student > Master 1 6%
Other 4 22%
Readers by discipline Count As %
Social Sciences 4 22%
Medicine and Dentistry 4 22%
Nursing and Health Professions 2 11%
Unspecified 2 11%
Economics, Econometrics and Finance 2 11%
Other 4 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2018.
All research outputs
#2,094,269
of 13,133,585 outputs
Outputs from BMC Health Services Research
#970
of 4,373 outputs
Outputs of similar age
#65,735
of 268,403 outputs
Outputs of similar age from BMC Health Services Research
#1
of 1 outputs
Altmetric has tracked 13,133,585 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,373 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,403 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them